TOPICS
Topic 1: Seamless Navigation between DFGs and Petri Nets using Time-Order Maps (Bachelor/Master)
Description: The directly-follows graph (DFG) is the most widely used process graph in the industry, yet it can lead to misleading analytical results if not well understood. This project investigates how visualization can reduce bias and improve traceability by enabling seamless navigation between two distinct process models: the DFG and the Petri net. We build on a similar visual technique, the Time-Order Map, and evaluate the results using benchmark event data. Basic knowledge of process mining or business process management (BPM) is preferable.
Initial References:
[1] C. Rubensson and J. Mendling, “Time-Order Map for Seamless Zooming between Process Models and Process Instances,” in 2025 7th International Conference on Process Mining (ICPM), Oct. 2025, pp. 1–8. URL: https://doi.org/10.1109/ICPM66919.2025.11220736 (alternative: https://drive.google.com/drive/folders/1fQq_pl9wjW7xTxZ5-PzmbF2g84iYvM4M on p.83).
[2] W. M. P. van der Aalst, “A practitioner’s guide to process mining: Limitations of the directly-follows graph,” Procedia Computer Science, vol. 164, pp. 321–328, 2019, URL: https://doi.org/10.1016/j.procs.2019.12.189.
Supervisor: Christoffer Rubensson
Topic 2: Visualizing Multi-Perspective Event Networks in Time-Order Maps (Bachelor/Master)
Description: The Time-Order Map is a visual technique that maps event networks in 2D coordinates and applies semantic zooming, allowing seamless navigation between the case view and the model view of event data. Currently, this visual technique is limited to event networks depicting only a single entity (e.g., an order in an order-to-cash process), not multiple ones (e.g., an order and a customer in an order-to-cash process). We extend the Time-Order Map to visualize multi-perspective event networks (that is, knowledge graphs) by adapting edge semantics and evaluating the results using benchmark event data. Basic knowledge of process mining or graph data is preferable.
Initial References:
[1] C. Rubensson and J. Mendling, “Time-Order Map for Seamless Zooming between Process Models and Process Instances,” in 2025 7th International Conference on Process Mining (ICPM), Oct. 2025, pp. 1–8. URL: https://doi.org/10.1109/ICPM66919.2025.11220736 (alternative URL: https://drive.google.com/drive/folders/1fQq_pl9wjW7xTxZ5-PzmbF2g84iYvM4M on p.83).
[2] S. Esser and D. Fahland, “Multi-Dimensional Event Data in Graph Databases,” Journal on Data Semantics, vol. 10, no. 1, pp. 109–141, Jun. 2021, URL: https://doi.org/10.1007/s13740-021-00122-1.
Supervisor: Christoffer Rubensson
Topic 3: Anthropomorphic Perceptions of Large Language Models: What is the gender of ChatGPT and its Counterparts? (Bachelor/Master)
Description: In the current digital era, large language models (LLMs) such as ChatGPT increasingly mediate human–technology interaction, often eliciting social and human-like interpretations of machine behavior. This thesis investigates these phenomena through the lens of anthropomorphism, understood as the tendency to attribute human characteristics to non-human entities. Specifically, the project examines laypeople’s underlying beliefs and implicit conceptions of LLMs, with a particular focus on the attribution of gender to systems such as ChatGPT.
Using a survey-based empirical design, the study aims to systematically assess how individuals perceive and conceptualize LLMs, and which assumptions they hold about their social and human-like qualities. By analyzing patterns of implicit gender attribution and related beliefs, the thesis seeks to contribute to a deeper understanding of how people cognitively and socially relate to contemporary AI systems. The findings are expected to inform broader discussions on anthropomorphism in human–AI interaction and its implications for the design, communication, and societal integration of AI technologies.
Initial References:
[1] Bazazi, S., Karpus, J., & Yasseri, T. (2025). AI’s assigned gender affects human-AI cooperation. iScience, 28(12). https://doi.org/10.1016/j.isci.2025.113905
[2] Ghosh, S., & Wilson, K. (2025). Bias Is a Math Problem, AI Bias Is a Technical Problem: 10-Year Literature Review of AI/LLM Bias Research Reveals Narrow [Gender-Centric] Conceptions of ‘Bias’, and Academia-Industry Gap. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8(2), 1091–1106. https://doi.org/10.1609/aies.v8i2.36613
[3] Masoudian, S., Escobedo, G., Strauss, H., & Schedl, M. (2025). Investigating Gender Bias in LLM-Generated Stories via Psychological Stereotypes (No. arXiv:2508.03292). arXiv. https://doi.org/10.48550/arXiv.2508.03292
[4] Aşkın, G., Saltık, İ., Boz, T. E., & Urgen, B. A. (2023). Gendered Actions with a Genderless Robot: Gender Attribution to Humanoid Robots in Action. International Journal of Social Robotics, 15(11), 1915–1931. https://doi.org/10.1007/s12369-022-00964-0
Supervisor: Jennifer Haase
Topic 4: Exploring the Phenomenon of AI Companions and Virtual Girlfriends: Understanding Human Attachment to Digital Partners (Master)
Description: With the rise of AI-driven virtual companions and “AI girlfriends” in apps like Replika, a new dimension of human-computer interaction is emerging, blurring the lines between emotional connection and digital simulation. This thesis seeks to explore the psychological, social, and technological aspects of these AI companions. Key questions include: What motivates individuals to seek relationships with AI partners? How do users form attachments to non-human entities? What role does personalization and anthropomorphism play in fostering these connections? By employing web-searches, qualitative interviews, surveys, or case studies, this research will aim to uncover the broader implications of AI companions on social behavior, emotional well-being, and the evolving nature of human entanglement with genAI tools.
Initial References:
[1] Chaturvedi, R., Verma, S., Das, R., & Dwivedi, Y. K. (2023). Social companionship with artificial intelligence: Recent trends and future avenues. Technological Forecasting and Social Change, 193, 122634.https://doi.org/10.1016/j.techfore.2023.122634
[2] Dang, J., & Liu, L. (2023). Do lonely people seek robot companionship? A comparative examination of the Loneliness–Robot anthropomorphism link in the United States and China. Computers in Human Behavior, 141, 107637. https://doi.org/10.1016/j.chb.2022.107637
[3] Strohmann, T., Siemon, D., Khosrawi-Rad, B., & Robra-Bissantz, S. (2023). Toward a design theory for virtual companionship. Human–Computer Interaction, 38(3–4), 194–234.https://doi.org/10.1080/07370024.2022.2084620
Supervisor: Jennifer Haase
Topic 5: Multi-LLM-Agent Process Simulation (Master)
Description: Process simulation is a central technique in business process management for analyzing what-if scenarios. Traditional business process simulation approaches rely on explicitly specified control-flow logic, which often leads to oversimplified task execution and limited representation of process dynamics. Recent advances in Large Language Models (LLMs) enable a new class of generative systems in which multiple LLM-based AI agents represent different process roles and interact to produce process outcomes. However, it remains largely unexplored how such LLM-based process simulations can be systematically designed and analyzed. The goal of this thesis is to design and implement a novel multi-LLM-agent process simulation that reproduces a small real-world business process and generates event logs suitable for process mining. The thesis will present a novel simulation artifact (e.g., a lightweight framework and prototype) and evaluate it against traditional business process simulation.
Initial References:
[1] Kirchdorfer, L., Blümel, R., Kampik, T., Van der Aa, H., and Stuckenschmidt, H. 2024. “AgentSimulator: An Agent-Based Approach for Data-Driven Business Process Simulation,” in 2024 6th International Conference on Process Mining (ICPM), pp. 97–104. (https://doi.org/10.1109/ICPM63005.2024.10680660).
[2] Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., Xu, F., and Li, Y. 2024. “Large Language Models Empowered Agent-Based Modeling and Simulation: A Survey and Perspectives,” Humanities and Social Sciences Communications (11:1), p. 1259. (https://doi.org/10.1057/s41599-024-03611-3).
[3] Sargent, R. G. 2013. “Verification and Validation of Simulation Models,” Journal of Simulation (7:1), pp. 12–24. (https://doi.org/10.1057/jos.2012.20).
Supervisor: Lennart Ebert
Topic 6: Predictive Process Monitoring (Master)
Description: Predictive Process Monitoring focuses on anticipating future states of running business processes (e.g., remaining time, next activities, or outcome) based on event data. Students who have taken the course on Process Prediction and Machine Learning are especially encouraged to apply with their own research ideas related to the course content.
Interested students are invited to submit a short pitch including:
- the research problem and question they would like to address, and
- references to relevant papers that motivate or relate to the idea.
Initial References:
[1] Di Francescomarino, C., Ghidini, C. (2022). Predictive Process Monitoring. In: van der Aalst, W.M.P., Carmona, J. (eds) Process Mining Handbook. Lecture Notes in Business Information Processing, vol 448. Springer, Cham. https://doi.org/10.1007/978-3-031-08848-3_10
Supervisor: Kate Revoredo